Results 251 to 260 of about 2,308,235 (326)
Quantitative Assessment of Biological Dynamics with Aggregate Data. [PDF]
McCoy S +6 more
europepmc +1 more source
Loss of Stat3 in Prx1+ Progenitors Impairs Molar Root Development
Stat3 critically regulates mandibular first molar and alveolar bone morphogenesis. Conditional ablation of Stat3 disrupts the osteogenic capacity of Prx1+ mesenchymal progenitors, as evidenced across in vivo and in vitro models. Abstract Signal Transducer and Activator of Transcription 3 (Stat3) acts as a central transcriptional modulator coordinating ...
Xin Feng +10 more
wiley +1 more source
Transcriptome-wide root causal inference. [PDF]
Strobl EV, Gamazon ER.
europepmc +1 more source
Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani +2 more
wiley +1 more source
Distinct glial functions are associated with Alzheimer's disease based on cell-type- and pathway-specific polygenic risk score analysis. [PDF]
Pratt T +8 more
europepmc +1 more source
Molecular dynamics simulations are advancing the study of ribonucleic acid (RNA) and RNA‐conjugated molecules. These developments include improvements in force fields, long‐timescale dynamics, and coarse‐grained models, addressing limitations and refining methods.
Kanchan Yadav, Iksoo Jang, Jong Bum Lee
wiley +1 more source
PCSK9 and breast cancer survival: a Mendelian Randomization study
Pott J +5 more
europepmc +1 more source
Genome-wide association study provides novel insight into the genetic architecture of severe obesity. [PDF]
Krishnan M +65 more
europepmc +1 more source
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani +4 more
wiley +1 more source

